ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences

Size: px

Start display at page:

Download "ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE. School of Mathematical Sciences"

Melvin Cross
5 years ago
Views:

! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-MATH-305 Introduction to Mathematical Computing 1.

05-15-13 Optional Course Designations: Yes No General Education Writing Intensive Honors Approval Request Date Approval Grant Date 2.

1 ! ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences New Revised COURSE: COS-MATH-305 Introduction to Mathematical Computing 1.0 Course designations and approvals: Required Course Approvals: Approval Approval Request Date Grant Date Academic Unit Curriculum Committee College Curriculum Committee Optional Course Designations: Yes No General Education Writing Intensive Honors Approval Request Date Approval Grant Date 2.0 Course information: Course Title: Introduction to Mathematical Computing Credit Hours: 2 Prerequisite(s): COS-MATH-219 Multivariable Calculus or COS-MATH-221 Multivariable and Vector Calculus, and CSCI-141 Computer Science I Co-requisite(s): None Course proposed by: School of Mathematical Sciences Effective date: Fall 2013 Contact Hours Maximum Students/section Classroom 2 33 Lab Workshop Other (specify) 2.1 Course conversion designation: (Please check which applies to this course) Semester Equivalent (SE) to: Semester Replacement (SR) to: Introduction to Symbolic Computing New 2.2 Semester(s) offered: Fall Spring Summer Offered every other year only Other Page 1 of 5

2 2.3 Student requirements: Students required to take this course: (by program and year, as appropriate) None Students who might elect to take the course: Students majoring or minoring in mathematics or mathematics-related fields 3.0 Goals of the course: (including rationale for the course, when appropriate) 3.1 To familiarize students with the capabilities and limitations of numerical and symbolic computing. 3.2 To provide an overview of basic commands and syntax for a mathematical computing language. 3.3 To provide students with the necessary programming and graphing skills needed for solving problems in mathematics. 3.4 To provide an opportunity for students to obtain a background in a scientific computing language necessary to use the language as a tool in future courses and in the workplace. 4.0 Course description: (as it will appear in the RIT Catalog, including pre- and co-requisites, semesters offered) COS-MATH-305 Introduction to Mathematical Computing This course is in an introduction to the use and application of scientific computing packages to graphically explore, numerically approximate, and symbolically compute solutions to problems arising in undergraduate courses in science, engineering and mathematics. Specific applications include numerical differentiation and integration, numerical optimization, initial value problems, linear systems of equations, and data fitting. (Pre-requisites: CSCI- 141 Computer Science I, and either COS-MATH-219 or COS-MATH-221.) Class 2, Credit 2 (S) 5.0 Possible resources: (texts, references, computer packages, etc.) 5.1 Attaway, S., MATLAB: A Practical Introduction to Programming and Problem Solving, Butterworth-Heinemann. 5.2 Davis, T., MATLAB Primer, Chapman & Hall/CRC. 5.3 Gilat, A., MATLAB: An Introduction with Applications, John Wiley & Sons, Inc. 5.4 Abell, M., and Braselton, J., Mathematica by Example, Academic Press. 5.5 Abell, M., and Braselton, J., Maple by Example, Academic Press. Page 2 of 5

3 6.0 Topics: (outline) Topics with an asterisk(*) are at the instructor s discretion, as time permits 6.1 Basics of a scientific computing language Overview of capabilities and syntax Numerical calculations and precision Introduction to available packages 6.2 Constructing programs in a scientific computing package Selection statements and loops Structured programming File input/output 6.3 Graphics and Visualization D plotting D plotting Image visualization Animation 6.4 Computing in Calculus and Differential Equations Limits of functions Derivatives, partial derivatives, total derivatives Function minimization Integration Initial value problems 6.5 Computing in Linear Algebra Matrix and vector computations Linear systems of equations 6.6 Scientific computing in Data Analysis Basic statistics, sets, sorting Data fitting Monte Carlo simulation 6.7 At least one of the following Eigenvector and eigenvalue computations Vectorization Interfacing with other languages Powering algorithms Extended Euclidean algorithm Factoring and primality testing Representations of graphs, and isomorphisms Brach and bound method for combinatorial optimization Graphical user interfaces Parallel processing Page 3 of 5

4 7.0 Intended learning outcomes and associated assessment methods of those outcomes: Assessment Methods Learning Outcomes 7.1 Students will explain basic commands and syntax in a scientific computing language 7.2 Students will demonstrate programming and graphing skills in a scientific computing language 7.1 Students will apply a scientific computing language as a tool in solving mathematical problems 8.0 Program goals supported by this course: 8.1 To develop students ability to solve problems using appropriate computational tools. 8.2 To develop students ability to model and analyze real-world problems. 9.0 General education learning outcomes and/or goals supported by this course: Assessment Methods General Education Learning Outcomes 9.1 Communication Express themselves effectively in common college-level written forms using standard American English Revise and improve written and visual content Express themselves effectively in presentations, either in spoken standard American English or sign language (American Sign Language or English-based Signing) Comprehend information accessed through reading and discussion 9.2 Intellectual Inquiry Review, assess, and draw conclusions about hypotheses and theories Page 4 of 5

5 Assessment Methods General Education Learning Outcomes Analyze arguments, in relation to their premises, assumptions, contexts, and conclusions Construct logical and reasonable arguments that include anticipation of counterarguments Use relevant evidence gathered through accepted scholarly methods and properly acknowledge sources of information 9.3 Ethical, Social and Global Awareness Analyze similarities and differences in human experiences and consequent perspectives Examine connections among the world s populations Identify contemporary ethical questions and relevant stakeholder positions 9.4 Scientific, Mathematical and Technological Literacy Explain basic principles and concepts of one of the natural sciences Apply methods of scientific inquiry and problem solving to contemporary issues Comprehend and evaluate mathematical and statistical information Perform college-level mathematical operations on quantitative data Describe the potential and the limitations of technology Use appropriate technology to achieve desired outcomes 9.5 Creativity, Innovation and Artistic Literacy Demonstrate creative/innovative approaches to coursebased assignments or projects Interpret and evaluate artistic expression considering the cultural context in which it was created 10.0 Other relevant information: (such as special classroom, studio, or lab needs, special scheduling, media requirements, etc.) 10.1 Class should be scheduled in the Statistics Lab 10.2 Computer laboratory facilities equipped with a scientific computing language Page 5 of 5

Self Study Report Computer Science

Self Study Report Computer Science Computer Science undergraduate students have access to undergraduate teaching, and general computing facilities in three buildings. Two large classrooms are housed in the Davis Centre, which hold about